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GlassHouse Cloud Services

GPU as a Service - H Series

GPU infrastructure hosted in Turkey, delivered from a highly available and KVKK-compliant cloud platform, purpose-built for your AI and high-performance computing workloads.

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GPU as a Service - H Series

ABOUT

Cloud-based GPU resources hosted entirely within Turkey and fully compliant with local regulations.

As GlassHouse Cloud, we deliver a secure, scalable, and truly differentiated technology experience through our GPU as a Service (GPUaaS) platform—hosted entirely in Turkey, fully compliant with KVKK and all sector-specific regulations, and backed by a high-availability architecture.

We reinforce data security with geographically redundant infrastructure and 24/7 monitoring, ensuring full compliance with all KVKK requirements. With GlassHouse Cloud GPU as a Service, you can confidently run AI workloads, large language models (LLMs), computer vision, video rendering, design and product development pipelines, scientific computing, and machine learning operations on NVIDIA’s latest GPU architecture.

With the GlassHouse Cloud platform and isolated GPU partitioning (MIG), we maximize both performance and security across your operational workflows.

Leave infrastructure management, system maintenance, version updates, and capacity planning to us—focus your time and resources on innovation with GlassHouse Cloud GPU as a Service!

ABOUT

FEATURES

Discover the features of our GPU as a Service - H Series.
Multi-Instance GPU (MIG) for multi-tenant environments:

Multi-Instance GPU (MIG) for multi-tenant environments:

By partitioning high-performance NVIDIA GPUs into isolated resource units, the infrastructure delivers maximum efficiency for multiple users and concurrent workloads.

Distributed Model Training:

Distributed Model Training:

Parallel and scalable training of models— including multi-stage LLM training—can be performed on NVIDIA’s multi-GPU H100 architecture, enabling efficient processing of large datasets.

LLM (Large Language Model) Training and Inference:

LLM (Large Language Model) Training and Inference:

With NVIDIA’s state-of-the-art GPUs, large-scale language models can be trained at high speed and leveraged for real-time inference.

NLP (Natural Language Processing):

NLP (Natural Language Processing):

Delivers high performance and accuracy for large-scale text analytics, sentiment detection, chatbot development, and document classification workloads.

Agentic AI, recommendation engines, and conversational AI:

Agentic AI, recommendation engines, and conversational AI:

Thanks to minimal latency and parallel GPU processing, intelligent recommendation and response systems can be efficiently executed on large-scale datasets.

Computer Vision:

Computer Vision:

Large-scale image and design processing, video analytics, and synthetic data generation are seamlessly handled by GPUs' high-throughput data processing capabilities, enabling the efficient execution of complex visual and multimedia workloads.

Real-Time Inference and Low-Latency Analytics:

Real-Time Inference and Low-Latency Analytics:

With NVIDIA’s next-generation accelerators, live systems can deliver reliable AI outputs for real-time decision-support applications.

Easy Integration and Access:

Easy Integration and Access:

GPU resources can be accessed remotely and effortlessly through interfaces such as SSH and APIs.

ADVANTAGES

Unveil the advantages of GlassHouse GPU as a Service - H Series!
ADVANTAGES

Unlock the True Potential of Artificial Intelligence!

 

High Performance and Enhanced Efficiency: NVIDIA GPUs deliver significantly higher speed and performance compared to traditional CPUs, executing complex AI/ML, LLM, Computer Vision, and NLP workloads in seconds through their parallel processing architecture. With MIG (Multi-Instance GPU) technology, GPUs can be segmented into isolated resource units, providing optimized performance for each workload. Thanks to automatic scaling, capacity can be increased or decreased instantly based on project demands—ensuring maximum efficiency across workloads.

 

Operational Simplicity and Cost Advantage: Instead of investing in your own GPU hardware, you can avoid high CAPEX costs as well as ongoing maintenance and management expenses by leveraging cloud-based GPU resources. GlassHouse handles all specialized operational processes—including hardware management, updates, cooling, energy optimization, and capacity planning—while your IT teams focus on strategic initiatives and innovation. With a pay-as-you-go model, you can maximize budget efficiency.

 

High Availability and Business Continuity: Operate seamlessly with a geographically redundant, 24/7 accessible infrastructure. Cloud-based GPU resources ensure continuous uptime through automated failover and high-availability architectures, safeguarding the resilience of your systems.

 

Data Security and Local Regulatory Compliance: GlassHouse GPUaaS is hosted in data centers within Turkey, fully meeting the requirements of regulation-heavy industries—such as finance and public sector—especially KVKK. Your sensitive and personal data is processed and protected in full compliance, backed by local operational assurance.

 

Easy Integration and Flexible Access: Access GPU resources remotely with ease through convenient interfaces such as SSH, APIs, and the cloud console. Achieve rapid workflow adaptation, agility in development processes, and seamless integration across applications.

 

REFERENCES

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Blog content that may be of interest to you

What is an NVIDIA GPU? NVIDIA GPU Guide
12 November 2025

What is an NVIDIA GPU? NVIDIA GPU Guide

NVIDIA is a leading technology company that manufactures powerful GPUs used in areas such as gaming, artificial intelligence, 3D modeling, and data processing. A GPU (Graphics Processing Unit) is a specialized processor designed to handle graphics and compute operations in parallel. NVIDIA GPUs are widely used for gaming, 3D rendering, video editing, AI workloads, and data science applications. The most common product families include GeForce (gaming), RTX Studio / Professional (professional design and workstations), the A Series and H100 (AI and HPC), Tesla (legacy), and NVIDIA DRIVE (autonomous driving). GPUs process data in parallel to compute lighting, shadows, textures, and effects in real time, while CUDA and Tensor Cores accelerate artificial intelligence workloads. The best GPU option depends on the intended use: GeForce for gaming, RTX Studio for 3D/animation, A Series and H100 for AI, and NVIDIA DRIVE for autonomous vehicles. Additionally, NVIDIA stands out with its AI software ecosystems and data center solutions, whereas AMD focuses on price-performance balance with its ROCm platform. Using NVIDIA GPUs in the cloud reduces hardware costs, provides scalability, delivers high performance, and enables remote access. In short, NVIDIA GPUs have become a critical hardware component for both individuals and enterprises by maximizing both graphics and compute performance in modern computing and data center environments. Start reading now to learn more!
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